Abstract
Crack detection is a critical task in routine inspection of building structures. Most of the traditional crack detection methodologies are conducted by human inspectors that may submit inaccurate damage assessments. In recent years, deep learning has produced extremely promising results for various tasks. In this work, a lightweight end-to-end pixel-wise classification architecture called SegNet is employed to segment the structure surface cracks. Compared with other semantic segmentation architectures, SegNet uses pooling indices calculated in the pooling step of the encoder to perform non-linear upsampling in the corresponding decoder, which doesn’t require to learn in the upsample. In this paper, a crack image dataset collected under a variety of complex environment are utilized to train and test the SegNet model, i.e. a self-labeled dataset with 2068 bridge cracks images at the size of \(1024\times 1024\). In order to improve the generalization ability of network data augmentation is used. The experimental results show that the SegNet outperforms the traditional edge detection algorithm, such as Canny and Sobel, in the dataset. The trained SegNet model can be used to segment the cracks in images at any size with the assistant of sliding window scanning technique.
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Acknowledgements
This work is financially supported in parts by the Fujian Provincial Department of Science and Technology of China (Grant No. 2019H0006 and 2018J01774), the National Natural Science Foundation of China (Grant No. 61601127), and the Foundation of Fujian Provincial Department of Industry and Information Technology of China (Grant No. 82318075).
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Song, C. et al. (2019). Pixel-Level Crack Detection in Images Using SegNet. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_22
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DOI: https://doi.org/10.1007/978-3-030-33709-4_22
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